Bottom Line:
At the same time the numbers of new antibiotics reaching the market have decreased.Within the frame of our study the effects of five different and well-known antibiotic classes on the bacterial metabolome were investigated both by intracellular fingerprint and extracellular footprint analysis.While cell cultures affected by antibiotics that act on intracellular targets showed class-specific fingerprints, the metabolic footprints differed significantly only when antibiotics that target the cell wall were applied.

Background: The emergence of antibiotic resistant pathogenic bacteria has reduced our ability to combat infectious diseases. At the same time the numbers of new antibiotics reaching the market have decreased. This situation has created an urgent need to discover novel antibiotic scaffolds. Recently, the application of pattern recognition techniques to identify molecular fingerprints in 'omics' studies, has emerged as an important tool in biomedical research and laboratory medicine to identify pathogens, to monitor therapeutic treatments or to develop drugs with improved metabolic stability, toxicological profile and efficacy. Here, we hypothesize that a combination of metabolic intracellular fingerprints and extracellular footprints would provide a more comprehensive picture about the mechanism of action of novel antibiotics in drug discovery programs.

Results: In an attempt to integrate the metabolomics approach as a classification tool in the drug discovery processes, we have used quantitative (1)H NMR spectroscopy to study the metabolic response of Escherichia coli cultures to different antibiotics. Within the frame of our study the effects of five different and well-known antibiotic classes on the bacterial metabolome were investigated both by intracellular fingerprint and extracellular footprint analysis. The metabolic fingerprints and footprints of bacterial cultures were affected in a distinct manner and provided complementary information regarding intracellular and extracellular targets such as protein synthesis, DNA and cell wall. While cell cultures affected by antibiotics that act on intracellular targets showed class-specific fingerprints, the metabolic footprints differed significantly only when antibiotics that target the cell wall were applied. In addition, using a training set of E. coli fingerprints extracted after treatment with different antibiotic classes, the mode of action of streptomycin, tetracycline and carbenicillin could be correctly predicted.

Conclusion: The metabolic profiles of E. coli treated with antibiotics with intracellular and extracellular targets could be separated in fingerprint and footprint analysis, respectively and provided complementary information. Based on the specific fingerprints obtained for different classes of antibiotics, the mode of action of several antibiotics could be predicted. The same classification approach should be applicable to studies of other pathogenic bacteria.

Fig7: Prediction of the antibiotic mode of action. PLS-DA scores plot of fingerprints of E. coli cultures incubated with a doxycycline, kanamycin and H2O for 30 min (6 components, R2 = 0.99, Q2 = 0.90). Using this model as reference data set fingerprints of cultures incubated with b tetracycline, c streptomycin and d carbenicillin were predicted according to their mode of action. Tetracycline and doxycycline resulted in similar fingerprints as well as kanamycin and streptomycin. Carbenicillin, an antibiotic which inhibits the cell wall showed strong similarities to the control group. N = 6 for each compound

Mentions:
A six component PLS-DA model, based on the intracellular metabolite profiles obtained for doxycycline, kanamycin, and control cells, was built to predict the response of E. coli to treatments with streptomycin, tetracycline, and carbenicillin (Fig. 7). The model showed a R2 and Q2 value of 99 and 90 % respectively. The degree of similarity was used to predict the likelihood of each of the latter having a similar mode of action to one of the former (doxycycline and tetracycline; kanamycin and streptomycin; carbenicillin as cell wall affecting antibiotic similar to the control). The probabilities that the fingerprints of streptomycin, tetracycline and carbenicillin belong to one of the clusters of doxycycline, kanamycin or of the control is summarized in Table 3. A 88.7 % prediction was calculated that the fingerprint of streptomycin is similar to that of kanamycin. The group of tetracycline showed 76.4 % probability of being similar to the profiles of doxycycline, and the fingerprint of carbenicillin matched those of the control with a probability of 62.1 %. In each case, the highest probability was assigned to the training class with the same known mode of action or in the case of carbenicillin with that of the control. The similarity of the response can be visualized by comparing the predicted scores of the test samples to those of the training samples (Fig. 7).Fig. 7

Fig7: Prediction of the antibiotic mode of action. PLS-DA scores plot of fingerprints of E. coli cultures incubated with a doxycycline, kanamycin and H2O for 30 min (6 components, R2 = 0.99, Q2 = 0.90). Using this model as reference data set fingerprints of cultures incubated with b tetracycline, c streptomycin and d carbenicillin were predicted according to their mode of action. Tetracycline and doxycycline resulted in similar fingerprints as well as kanamycin and streptomycin. Carbenicillin, an antibiotic which inhibits the cell wall showed strong similarities to the control group. N = 6 for each compound

Mentions:
A six component PLS-DA model, based on the intracellular metabolite profiles obtained for doxycycline, kanamycin, and control cells, was built to predict the response of E. coli to treatments with streptomycin, tetracycline, and carbenicillin (Fig. 7). The model showed a R2 and Q2 value of 99 and 90 % respectively. The degree of similarity was used to predict the likelihood of each of the latter having a similar mode of action to one of the former (doxycycline and tetracycline; kanamycin and streptomycin; carbenicillin as cell wall affecting antibiotic similar to the control). The probabilities that the fingerprints of streptomycin, tetracycline and carbenicillin belong to one of the clusters of doxycycline, kanamycin or of the control is summarized in Table 3. A 88.7 % prediction was calculated that the fingerprint of streptomycin is similar to that of kanamycin. The group of tetracycline showed 76.4 % probability of being similar to the profiles of doxycycline, and the fingerprint of carbenicillin matched those of the control with a probability of 62.1 %. In each case, the highest probability was assigned to the training class with the same known mode of action or in the case of carbenicillin with that of the control. The similarity of the response can be visualized by comparing the predicted scores of the test samples to those of the training samples (Fig. 7).Fig. 7

Bottom Line:
At the same time the numbers of new antibiotics reaching the market have decreased.Within the frame of our study the effects of five different and well-known antibiotic classes on the bacterial metabolome were investigated both by intracellular fingerprint and extracellular footprint analysis.While cell cultures affected by antibiotics that act on intracellular targets showed class-specific fingerprints, the metabolic footprints differed significantly only when antibiotics that target the cell wall were applied.

Background: The emergence of antibiotic resistant pathogenic bacteria has reduced our ability to combat infectious diseases. At the same time the numbers of new antibiotics reaching the market have decreased. This situation has created an urgent need to discover novel antibiotic scaffolds. Recently, the application of pattern recognition techniques to identify molecular fingerprints in 'omics' studies, has emerged as an important tool in biomedical research and laboratory medicine to identify pathogens, to monitor therapeutic treatments or to develop drugs with improved metabolic stability, toxicological profile and efficacy. Here, we hypothesize that a combination of metabolic intracellular fingerprints and extracellular footprints would provide a more comprehensive picture about the mechanism of action of novel antibiotics in drug discovery programs.

Results: In an attempt to integrate the metabolomics approach as a classification tool in the drug discovery processes, we have used quantitative (1)H NMR spectroscopy to study the metabolic response of Escherichia coli cultures to different antibiotics. Within the frame of our study the effects of five different and well-known antibiotic classes on the bacterial metabolome were investigated both by intracellular fingerprint and extracellular footprint analysis. The metabolic fingerprints and footprints of bacterial cultures were affected in a distinct manner and provided complementary information regarding intracellular and extracellular targets such as protein synthesis, DNA and cell wall. While cell cultures affected by antibiotics that act on intracellular targets showed class-specific fingerprints, the metabolic footprints differed significantly only when antibiotics that target the cell wall were applied. In addition, using a training set of E. coli fingerprints extracted after treatment with different antibiotic classes, the mode of action of streptomycin, tetracycline and carbenicillin could be correctly predicted.

Conclusion: The metabolic profiles of E. coli treated with antibiotics with intracellular and extracellular targets could be separated in fingerprint and footprint analysis, respectively and provided complementary information. Based on the specific fingerprints obtained for different classes of antibiotics, the mode of action of several antibiotics could be predicted. The same classification approach should be applicable to studies of other pathogenic bacteria.